maca 3.3.0编译报错 提示“'tuple' file not found”

基于maca开发一个简单的测试程序，但是编译时报错：
mxcc -std=c++11 -o 04-vram_leak_sim 04-vram_leak_sim.cu
In file included from <built-in>:1:
In file included from /opt/maca-3.3.0/mxgpu_llvm/lib/clang/19/include/__clang_maca_runtime_wrapper.h:25:
In file included from /opt/maca-3.3.0/mxgpu_llvm/lib/clang/19/include/__clang_maca_atomic_functions.h:18:
In file included from /opt/maca-3.3.0/include/common/__clang_maca_mckl_lib_header.h:25:
In file included from /opt/maca-3.3.0/include/mcr/mc_reference_surface_types.h:12:
In file included from /opt/maca-3.3.0/include/mcr/mc_runtime_api.h:7579:
/opt/maca-3.3.0/include/mcr/mc_runtime_api_template_wrapper.h:3:10: fatal error: 'tuple' file not found
3 | #include <tuple>
| ^~~
1 error generated when compiling for xcore1000.

使用的CPU为metax C500

尊敬的开发者您好，请您提供以下信息
一、软硬件信息
1.服务器厂家:
2.沐曦GPU型号：
3.操作系统内核版本：
4.是否开启CPU虚拟化：
5.mx-smi回显：
二、问题现象
请描述详细的问题现象日志。若日志过长，请上传附件(txt格式）。

1.服务器厂家: 浪潮信息 NF5468M7
2.沐曦GPU型号：MX C500
3.操作系统内核版本：KeyarchOS v5
4.是否开启CPU虚拟化：已开启，CPU信息如下：
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 52 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 128
On-line CPU(s) list: 0-127
Vendor ID: GenuineIntel
BIOS Vendor ID: Intel(R) Corporation
Model name: INTEL(R) XEON(R) GOLD 6530
BIOS Model name: INTEL(R) XEON(R) GOLD 6530 CPU @ 2.1GHz
BIOS CPU family: 179
CPU family: 6
Model: 207
Thread(s) per core: 2
Core(s) per socket: 32
Socket(s): 2
Stepping: 2
CPU(s) scaling MHz: 23%
CPU max MHz: 4000.0000
CPU min MHz: 800.0000
BogoMIPS: 4200.00
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm
pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid ap
erfmperf tsc_known_freq pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 ss
e4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_
l3 cat_l2 cdp_l3 intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fs
gsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clf
lushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm
total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp
epp hwp_pkg_req vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg t
me avx512_vpopcntdq rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig
arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization features:
Virtualization: VT-x
Caches (sum of all):
L1d: 3 MiB (64 instances)
L1i: 2 MiB (64 instances)
L2: 128 MiB (64 instances)
L3: 320 MiB (2 instances)
NUMA:
NUMA node(s): 2
NUMA node0 CPU(s): 0-31,64-95
NUMA node1 CPU(s): 32-63,96-127
Vulnerabilities:
Gather data sampling: Not affected
Itlb multihit: Not affected
L1tf: Not affected
Mds: Not affected
Meltdown: Not affected
Mmio stale data: Not affected
Reg file data sampling: Not affected
Retbleed: Not affected
Spec rstack overflow: Not affected
Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Srbds: Not affected
Tsx async abort: Not affected

5.mx-smi回显：
mx-smi version: 2.2.1

=================== MetaX System Management Interface Log ===================
Timestamp : Tue Dec 16 10:35:48 2025

Attached GPUs : 1
+---------------------------------------------------------------------------------+
| MX-SMI 2.2.1 Kernel Mode Driver Version: 2.12.0 |
| MACA Version: 3.3.0.15 BIOS Version: 1.23.1.0 |
|------------------------------------+---------------------+----------------------+
| GPU NAME | Bus-id | GPU-Util |
| Temp Pwr:Usage/Cap | Memory-Usage | |
|====================================+=====================+======================|
| 0 MetaX C500 | 0000:58:00.0 | 0% |
| 44C 32W / 350W | 858/65536 MiB | |
+------------------------------------+---------------------+----------------------+

+---------------------------------------------------------------------------------+
| Process: |
| GPU PID Process Name GPU Memory |
| Usage(MiB) |
|=================================================================================|
| no process found |
+---------------------------------------------------------------------------------+

End of Log

错误提示如下：
mxcc -std=c++20 -o 04-vram_leak_sim 04-vram_leak_sim.cu
In file included from <built-in>:1:
In file included from /opt/maca-3.3.0/mxgpu_llvm/lib/clang/19/include/__clang_maca_runtime_wrapper.h:25:
In file included from /opt/maca-3.3.0/mxgpu_llvm/lib/clang/19/include/__clang_maca_atomic_functions.h:18:
In file included from /opt/maca-3.3.0/include/common/__clang_maca_mckl_lib_header.h:25:
In file included from /opt/maca-3.3.0/include/mcr/mc_reference_surface_types.h:12:
In file included from /opt/maca-3.3.0/include/mcr/mc_runtime_api.h:7579:
/opt/maca-3.3.0/include/mcr/mc_runtime_api_template_wrapper.h:3:10: fatal error: 'tuple' file not found
3 | #include <tuple>
| ^~~
1 error generated when compiling for xcore1000.

尊敬的开发者您好，您的cuda代码可以发出来了吗

代码如下：

#include <iostream>
#include <vector>
#include <chrono>
#include <thread>
#include <cuda_runtime.h>

// CUDA错误检查宏
void checkCudaError(cudaError_t err, const char* msg) {
    if (err != cudaSuccess) {
        std::cerr << "CUDA Error: " << msg << " - " << cudaGetErrorString(err) << std::endl;
        exit(EXIT_FAILURE);
    }
}

int main() {
    std::cout << "=== MetaX C500 VRAM Leak Simulation ===" << std::endl;
    std::cout << "WARNING: This program will intentionally leak GPU memory!" << std::endl;
    std::cout << "Press Ctrl+C to stop the test." << std::endl << std::endl;

    // 获取设备信息
    int deviceCount;
    checkCudaError(cudaGetDeviceCount(&deviceCount), "cudaGetDeviceCount");
    if (deviceCount == 0) {
        std::cerr << "Error: No CUDA-compatible GPU found!" << std::endl;
        return 1;
    }

    // 选择GPU设备（可根据需要修改设备ID）
    int deviceId = 0;
    checkCudaError(cudaSetDevice(deviceId), "cudaSetDevice");

    cudaDeviceProp prop;
    checkCudaError(cudaGetDeviceProperties(&prop, deviceId), "cudaGetDeviceProperties");
    std::cout << "Target Device: " << prop.name << std::endl;
    std::cout << "Total VRAM: " << (prop.totalGlobalMem / 1024 / 1024) << " MB" << std::endl;
    std::cout << std::endl;

    // 配置参数
    const size_t ALLOCATION_SIZE = 50 * 1024 * 1024; // 每次分配50MB
    const int ALLOCATIONS_PER_BATCH = 10;            // 每批分配次数
    const int BATCH_DELAY_MS = 500;                  // 批次间延迟（毫秒）

    std::vector<void*> allocations;
    size_t totalAllocated = 0;
    int allocationCount = 0;

    try {
        while (true) {
            // 批量分配显存
            for (int i = 0; i < ALLOCATIONS_PER_BATCH; ++i) {
                void* ptr = nullptr;
                cudaError_t err = cudaMalloc(&ptr, ALLOCATION_SIZE);

                if (err != cudaSuccess) {
                    std::cerr << "\n[ERROR] Allocation failed!" << std::endl;
                    std::cerr << "  Allocation number: " << allocationCount << std::endl;
                    std::cerr << "  Total allocated: " << (totalAllocated / 1024 / 1024) << " MB" << std::endl;
                    std::cerr << "  CUDA Error: " << cudaGetErrorString(err) << std::endl;

                    // 显示近似剩余显存
                    size_t free, total;
                    cudaMemGetInfo(&free, &total);
                    std::cerr << "  Approximate free VRAM: " << (free / 1024 / 1024) << " MB" << std::endl;

                    throw std::runtime_error("Out of memory");
                }

                allocations.push_back(ptr);
                totalAllocated += ALLOCATION_SIZE;
                allocationCount++;
            }

            // 打印进度
            std::cout << "[LEAK] Allocations: " << allocationCount
                      << " | Total: " << (totalAllocated / 1024 / 1024) << " MB"
                      << std::endl;

            // 延迟一段时间，便于观察
            std::this_thread::sleep_for(std::chrono::milliseconds(BATCH_DELAY_MS));
        }
    }
    catch (const std::exception& e) {
        std::cerr << "\nTest completed: " << e.what() << std::endl;
    }

    // 说明：程序故意不释放内存以模拟泄漏
    // 问题：进程退出后GPU驱动会自动回收显存么？

    std::cout << "\n=== Test Finished ===" << std::endl;
    std::cout << "To clean up: Terminate this process (Ctrl+C) or let it crash." << std::endl;
    std::cout << "Driver will automatically free leaked memory on process exit." << std::endl;

    return 0;
}